Uncertainty Factors Research Articles

Cost and competitiveness of green hydrogen and the effects of the European Union regulatory framework

By passing the delegated acts supplementing the revised Renewable Energy Directive, the European Commission has recently set a regulatory benchmark for the classification of green hydrogen in the European Union. Controversial reactions to the restricted power purchase for electrolyser operation reflect the need for more clarity about the effects of the delegated acts on the cost and the renewable characteristics of green hydrogen. To resolve this controversy, we compare different power purchase scenarios, considering major uncertainty factors such as electricity prices and the availability of renewables in various European locations. We show that the permission for unrestricted electricity mix usage does not necessarily lead to an emission intensity increase, partially debilitating concerns by the European Commission, and could notably decrease green hydrogen production cost. Furthermore, our results indicate that the transitional regulations adopted to support a green hydrogen production ramp-up can result in similar cost reductions and ensure high renewable electricity usage.

Robust topology optimization for multi-material structures considering material uncertainties

Multi-material structures have attracted more and more attention from scholars and engineers, because they usually have good composite performance. Uncertainty factors are ubiquitous in practice, especially for material uncertainties. Multi-material structures contain many materials with different properties, material uncertainties will affect the allocation of material usage, thereby affecting structural performance. Therefore, it is crucial to consider material uncertainties in the design of multi-material structures. This paper detailedly explores the impacts of elastic modulus uncertainty and Poisson's ratio uncertainty on the design of multi-material structures. The topology optimization is to minimize the compliance subjected to a mass constraint, where multiple materials can be freely allocated during the optimization. Floating projection topology optimization is adopted to search for the structural topologies, and robust sensitivity information is derived. For the quantification of material uncertainties, this paper introduces a non-intrusive polynomial chaos expansion (PCE) method to implicitly quantify them, the computational efficiency and accuracy in PCE is compared with the classical Monte Carlo method. Finally, topology optimization examples are provided to demonstrate the effects of elastic modulus uncertainty, Poisson's ratio uncertainty and hybrid uncertainty on the design of multi-material structures.

A weight of evidence evaluation of the mode of action of isoeugenol

Isoeugenol is one of several phenylpropenoid compounds that is used as a fragrance, food flavoring agent and in aquaculture as a fish anesthetic. Carcinogenicity testing in rats and mice by NTP resulted in clear evidence of carcinogenicity (hepatic adenomas/carcinomas) in male mice only. A nongenotoxic threshold mode of action (MOA) is postulated for isoeugenol and is discussed considering the IPCS MOA and Human Relevance Framework. The weight of evidence indicates that isoeugenol is not genotoxic and that the carcinogenic outcome in male mice relates directly to the metabolism of individual compounds. Benchmark Dose (BMD) modeling was conducted to determine a Point of Departure (POD) and potential threshold of carcinogenicity. The results of the BMD evaluation for isoeugenol resulted in an estimated POD for carcinogenicity in the male mouse of 8 mg/kg with a lower limit of 4 mg/kg, representing a POD for the determination of an acceptable daily intake. With application of uncertainty factors, an ADI of 40 μg/kg is calculated. This daily dose in humans would be protective of human health, including carcinogenicity. A corresponding maximum residual level (MRL) of 3200 μg/kg fish is also estimated based on this POD that considers the threshold MOA.

Tail risk intersection between tech-tokens and tech-stocks

This study investigates the interconnectedness of upside and downside tail risks between tech-industry tokens and equities, focusing on uncertainties and portfolio implications. We estimate tail-risk measures at various quantiles using the conditional autoregressive value-at-risk method and investigate selected assets' static and dynamic connectedness using a time-varying parameter-vector autoregression approach. The findings reveal a moderate level of time-varying tail-risk interconnectedness. Furthermore, we examine the role of global uncertainty factors in tail-risk contagion and identify the factors driving connectedness. Moreover, this study analyzes portfolio implications, offering insights into effective risk management strategies and the potential benefits of diversification. These findings have significant implications for investors, regulators, and policymakers in risk management in the technology industry.

A SENSITIVITY ANALYSIS METHOD COMBINING DEMPSTER-SHAFER THEORY AND MACHINE LEARNING FOR ENERGY-SAVING EVALUATION OF BUILDING OCCUPANT BEHAVIOR

ABSTRACT For a very long time, the research of the sensitivity analysis of occupant behavior to energy assessment has been in the spotlight. The key element of the research is determining the exact probability of occupant behavior uncertainty. However, due to the specificity of occupant behavior, data on occupant behavior from different independent sources of information can differ significantly. This paper explores the use of Dempster-Shafer theory to the sensitivity analysis of energy evaluation of occupant behavior in buildings. The Dempster-Shafer theory is an imprecise probability theory that allows the system to create assumed confidence intervals based on interval values probability combined with knowledge of uncertainty factors from many different sources of information. The findings show that the data processing approach based on Dempster-Shafer theory provides effective and reliable information for evaluating energy related to human behavior in buildings. To begin with, the sensitivity analysis process might be accelerated by applying machine learning to process the data. Then, in order to ensure the accuracy of the simulation results, multiple learning methods can be used. Finally, in this paper, model parameters were chosen based on the specific circumstances as soon as the model had been built in order to effectively reduce costs related to operation and increase model accuracy. To establish the final results, the model is evaluated using global sensitivity analysis methods.

A qualitative risk assessment model for water reuse: Risks related to agricultural irrigation in Brazil

Urbanization and industrialization are increasing extreme weather events, causing water quantity and quality reduction. Global water scarcity impacts 32.5 % of the urban population and is growing. Brazil has also witnessed water scarcity, notably in the southeast (2014–2015) and south (2019–2020), with reservoirs dropping below 20 % capacity. Water reuse is vital for mitigating scarcity, though it presents risks due to contaminants. Risk analysis studies are crucial for evaluating contamination sources, pathways, and exposure scenarios in water reuse practices. Various methodologies, including quantitative, semi-quantitative, and qualitative analyses, can be employed. Given the uncertainty and diverse factors, qualitative methods are recommended for non-potable water reuse risk analysis. This work presents a qualitative risk analysis methodology that allows to evaluate non-potable water reuse categories. It assesses factors affecting human health and the environment, considering exposure scenarios, characteristics of the receptors, and sources of reused water. The risk analysis of water reuse was carried out focusing on agricultural reuse, considering as alternatives the irrigation of soybean and sugarcane crops. By reviewing literature, the probability of occurrence and the magnitude of impact of the risk factors were identified and rated, using an increasing relative numeric scale. This process resulted in an overall risk value for comparing agricultural irrigation alternatives. The obtained results indicate a promising risk analysis model that can be adjusted and applied to various water reuse modalities and key factors. This adaptable risk analysis model is mainly related to water treatment methods, prompting the proposal of risk control measures.

Projections and uncertainties of winter windstorm damage in Europe in a changing climate

Abstract. Winter windstorms are among the most significant natural hazards in Europe linked to fatalities and substantial damage. However, projections of windstorm impact in Europe under climate change are highly uncertain. This study combines climate projections from 30 general circulation models participating in Phase 6 of the Coupled Model Intercomparison Project (CMIP6) with the climate risk assessment model CLIMADA to obtain projections of windstorm-induced damage over Europe in a changing climate. We conduct an uncertainty–sensitivity analysis and find large uncertainties in the projected changes in the damage, with climate model uncertainty being the dominant factor of uncertainty in the projections. We investigate the spatial patterns of the climate change-induced modifications in windstorm damage and find an increase in the damage in northwestern and northern central Europe and a decrease over the rest of Europe, in agreement with an eastward extension of the North Atlantic storm track into Europe. We combine all 30 available climate models in an ensemble-of-opportunity approach and find evidence for an intensification of future climate windstorm damage, in which damage with return periods of 100 years under current climate conditions becomes damage with return periods of 28 years under future SSP585 climate scenarios. Our findings demonstrate the importance of climate model uncertainty for the CMIP6 projections of windstorms in Europe and emphasize the increasing need for risk mitigation due to extreme weather in the future.

Developing readiness levels for risk assessment in green transition engineering projects

AbstractThis paper aims to develop a risk assessment framework that addresses both the complexities of the risk landscape that green transition portfolios face, but is recognizable and easily understandable by stakeholders. For this purpose, we build upon the framework of NASA Technology Readiness Levels (TRLs). This study analyzes six existing readiness levels framework that are held towards uncertainty factors from the Green Transition. The TRL scale are coupled with Risk, Uncertainty, and Ignorance to score the individual level of uncertainty. The paper ends with suggestion for further studies.

Application of Monte Carlo simulation for the estimation of production availability in geothermal well

Wellbore simulation is one of the methods used in geothermal system performance analysis using a mathematical model of the geothermal system that considers parameters such as operating conditions, feed zone parameters, and well geometry. One of the methods used to perform wellbore simulation is the homogenous method. However, the results may still have a high level of uncertainty. To overcome this, probabilistic methods such as the Monte Carlo method can improve the accuracy of geothermal well production capacity modelling. The Monte Carlo method determines variability and uncertainty in the data being analysed. In determining geothermal well production capacity, the Monte Carlo method can take random samples of various influencing parameters, such as operating conditions, feed zone parameters, and well geometry. By conducting a Monte Carlo simulation, a more comprehensive probability distribution related to well production capacity will be obtained, considering the uncertainties associated with the available data. This research aims to determine the probability of production capacity of a candidate well in a geothermal field by considering uncertainty factors. In this research, a simulation is carried out until it converges using the Monte Carlo method so that the probability value will be obtained. Based on the analysis using the Montecarlo method, the probability of production capacity at the VY-1 Well of Vyatra Field is obtained, namely the P10 value with a large Mwe of 8.31 with an error of 0.102. Next, the P50 value obtained a Mwe value of 11.44 with an error of 0.001. Then P90 was obtained at 14.44 with an error of 0.012. Based on the analysis results, the most influential parameter in determining the production capacity of a Geothermal well is the productivity index value, followed by temperature and reservoir pressure.

Preparation of final qualifying work at a university: the view of modern students

The problem and the aim of the study. The need to study and use the capabilities of network technologies to improve general cultural and professional levels is becoming a condition for training highly qualified specialists. To effectively use computer devices and digital tools, computational thinking is required. The purpose of the study is to explore the possibilities of using a professionally oriented network course for the development of computational thinking of engineers. Research methods. Computational thinking is considered by the authors as a mental process consisting of the sequential execution of a set of actions: activation from human memory of a system of images of objects, and connections between them; formulation of the problem taking into account uncertainty factors; drawing up a solution algorithm and its effective implementation. 68 bachelors from the first year of the Russian State Agrarian University named after K. A. Timiryazev were involved in the study (Russian Federation). Training program 23.03.01 – “Technology of transport processes”, profile – “Digital transport and logistics systems of road transport”. The online training course “Computer Science and Networks in the Industry” was developed by E. V. Shchedrina, and presented on the Moodle platform. Registration certificate No. 24877 dated August 28, 2021. To diagnose and assess the maturity of computational thinking, the author’s testing materials are used. For statistical processing of data, the Pearson's chisquared test was used. Results. A characteristic of the network resource is the execution of all calculation tasks on a computer using application software. When working with course materials, engineers perform a sequence of actions characteristic of computational thinking: analyze the text of a professionally oriented task (formulates the task as a computational problem), decompose the problem; compose and implement an algorithm, perform its analysis and evaluation. Statistically significant differences were determined in the qualitative changes that occurred in the system of training specialists (χ2 = 6.842; p < 0.05). In conclusion, the positive aspects of using a professionally oriented network course for the development of computational thinking of engineers are highlighted (for example, to gain experience in formulating a problem taking into account the uncertainty of the future, students analyze a corporate network, determine a subnet mask for different conditions, etc.).

Professionally oriented network course as the basis for developing the computational thinking of future engineers

The problem and the aim of the study. The need to study and use the capabilities of network technologies to improve general cultural and professional levels is becoming a condition for training highly qualified specialists. To effectively use computer devices and digital tools, computational thinking is required. The purpose of the study is to explore the possibilities of using a professionally oriented network course for the development of computational thinking of engineers. Research methods. Computational thinking is considered by the authors as a mental process consisting of the sequential execution of a set of actions: activation from human memory of a system of images of objects, and connections between them; formulation of the problem taking into account uncertainty factors; drawing up a solution algorithm and its effective implementation. 68 bachelors from the first year of the Russian State Agrarian University named after K. A. Timiryazev were involved in the study (Russian Federation). Training program 23.03.01 – “Technology of transport processes”, profile – “Digital transport and logistics systems of road transport”. The online training course “Computer Science and Networks in the Industry” was developed by E. V. Shchedrina, and presented on the Moodle platform. Registration certificate No. 24877 dated August 28, 2021. To diagnose and assess the maturity of computational thinking, the author’s testing materials are used. For statistical processing of data, the Pearson's chisquared test was used. Results. A characteristic of the network resource is the execution of all calculation tasks on a computer using application software. When working with course materials, engineers perform a sequence of actions characteristic of computational thinking: analyze the text of a professionally oriented task (formulates the task as a computational problem), decompose the problem; compose and implement an algorithm, perform its analysis and evaluation. Statistically significant differences were determined in the qualitative changes that occurred in the system of training specialists (χ2 = 6.842; p < 0.05). In conclusion, the positive aspects of using a professionally oriented network course for the development of computational thinking of engineers are highlighted (for example, to gain experience in formulating a problem taking into account the uncertainty of the future, students analyze a corporate network, determine a subnet mask for different conditions, etc.).

Learning-based model predictive secondary frequency control of PV-ESS-EV microgrid

Microgrids comprising photovoltaic (PV), energy-storage systems and electric vehicles have received increasing attention. However, the increase in renewable energy sources, such as PVs, will introduce uncertainty factors to the frequency response, resulting in frequency fluctuations that can reduce power quality. Hence, we propose an outstanding learning-based control approach that combines a model of physical principles with a Gaussian Process (GP) learning approach. The learning model corrects the physical principles model and estimates its uncertainty. Simulations are performed by comparing the proposed controller with conventional model-predictive controllers. The results indicate that the learning-based controller proposed can adjust the frequency with less overshooting and a lower root mean square error compared to the conventional MPC control method.

Selection of an economics-energy-environment scheduling strategy for a community virtual power plant considering decision-makers’ risk attitudes based on improved information gap decision theory

To address the impact of decision-makers' risk attitudes (DMRA) and uncertainties on dispatch strategy in community integrated energy systems, a multi-objective dispatch model of a community virtual power plant (CVPP) is proposed. Firstly, a novel CVPP model that considers DMRA and a multi-objective satisfaction model of economics-energy-environment are built. Secondly, the information gap decision theory (IGDT) model is improved by considering uncertainties of renewable energy, loads, and DMRA. Thirdly, the VIKOR method is improved by using the weights from multiple objective functions derived by combining the entropy weight and full consistency method. And the conservative and risky strategy selection methods in the VIKOR method are used to select the optimal strategy from the obtained multi-objective Pareto solution set that satisfies decision-makers with risk preferences. Thus, the impact of DMRA on optimal strategy selection is fully described. Finally, the proposed model's effectiveness is validated using a multi-scenario example of a residential area. The results indicate that: 1) The novel CVPP provides realistic scheduling strategies based on the DMRA. 2) After implementing demand response, the resident cost and net carbon emissions are reduced by 9 % and 91 %, respectively. Energy supplier profit and renewable energy utilization rate are increased. The constructed IGDT model also improves multiple objectives. 3) The improved IGDT model's uncertainty and deviation factors allow for diverse scheduling strategies. Simultaneously, the improved VIKOR method provides a new method for decision-makers to select strategies. The model is a guide for selecting scheduling strategies and a way of encouraging the usage of renewable energy.

Coordinated optimal operation of integrated electrical and transportation network considering source-load uncertainties in severe weather scenarios

The large-scale penetration of electric vehicles and the emerging dynamic wireless charging technology strengthen the coupling between distribution networks and transportation networks. However, the randomness of travels in the transportation network, the fluctuation of the output of clean energy such as wind power and photovoltaic, and the sudden change of weather will have a significant impact on the safe and stable operation of the two networks. In this context, based on the background of wireless charging technology, a cooperative optimal operation scheduling model of electric transportation integrated network is proposed, which considers the uncertainty of traffic demand and photovoltaic prediction error, and regards it as the uncertainty factor of load side and source side of power grid. The Information gap decision theory (IGDT) is introduced to model the bilateral uncertainty of the network, and the corresponding risk avoidance model and risk speculation model are obtained. Furthermore, considering the effect of inclement weather scenarios on the uncertain factors of road traffic and charging facilities, a modified integrated network model of electric traffic under inclement weather scenarios is proposed. Finally, a case study is carried out on a test system consisting of a 12-node transportation network and a 20-node power distribution network. The results show that the information gap decision theory can effectively deal with the changes of dual networks under multiple uncertain scenarios, form a risk decision-making model, and provide theoretical support for different decision-making methods.

Navigating environmental concerns and geopolitical risks: A study on green sukuk and Islamic equities amid climate crisis and the Russia-Ukraine conflict

Investors seek safe haven assets for portfolio diversification and risk management because climate change and environmental markets have recently become the concern of many governments, companies, households, politicians and academics. Therefore, this study examines the dynamic connectedness between sukuk (Islamic bonds), green sukuk, Islamic equities, and Islamic green markets for equity by comparing both in the short and long terms spillover and examining the effects of uncertainty factors on dynamic connectedness at different spillover horizons. In the short run, green Islamic equities, green sukuk and sukuk are net transmitters, and Islamic equities are net receivers. However, in the medium and long term, the behavior of these markets is vice versa. We then find the determinants of connectivity levels using macroeconomic conditions. We find that some of the determinants that cause return spillover in the short and medium term are quite different from those that cause return spillover in the long term. The results of portfolio analysis show that green sukuk are effective in hedging the risks of Islamic equities, green Islamic equities and sukuk. The performance characteristics of these potential diversification and risk mitigation benefits have been robust and enduring during the Covid-19 pandemic and the Russia-Ukraine War. Finally, our findings are of great importance to investors interested in ethical investing and for policymakers to maintain a healthy and resilient financial system.

A Two-Stage Robust Pricing Strategy for Electric Vehicle Aggregators Considering Dual Uncertainty in Electricity Demand and Real-Time Electricity Prices

To enable the regulation and utilization of electric vehicle (EV) load resources by the power grid in the electricity market environment, a third-party electric vehicle aggregator (EVA) must be introduced. The strategy of EVA participation in the electricity market must be studied. During operation, the EVA faces a double uncertainty in the market, namely, electricity demand and electricity price, and must optimize its market behavior to protect its own interests. To achieve this goal, we propose a robust pricing strategy for the EVA that takes into account the coordination of two-stage market behavior to enhance operational efficiency and risk resistance. A two-stage robust pricing strategy that takes into account uncertainty was established by first considering day-ahead pricing, day-ahead electricity purchases, real-time electricity management, and EV customer demand response for the EVA, and further considering the uncertainty in electricity demand and electricity prices. The two-stage robust pricing model was transformed into a two-stage mixed integer programming by linearization method and solved iteratively by the columns and constraints generation (CCG) algorithm. Simulation verification was carried out, and the results show that the proposed strategy fully considers the influence of price uncertainty factors, effectively avoids market risks, and improves the adaptability and economy of the EVA’s business strategy.

A Physics-Based Tweedie Exponential Dispersion Process Model for Metal Fatigue Crack Propagation and Prognostics

Most structural faults in metal parts can be attributed to fatigue crack propagation. The analysis and prognostics of fatigue crack propagation play essential roles in the health management of mechanical systems. Due to the impacts of different uncertainty factors, the crack propagation process exhibits significant randomness, which causes difficulties in fatigue life prediction. To improve prognostic accuracy, a physics-based Tweedie exponential dispersion process (TEDP) model is proposed via integrating Paris Law and the stochastic process. This TEDP model can capture both the crack growth mechanism and uncertainty. Compared with other existing models, the TEDP taking Wiener process, Gamma process, and inverse process as special cases is more general and flexible in modeling complex degradation paths. The probability density function of the model is derived based on saddle-joint approximation. The unknown parameters are calculated via maximum likelihood estimation. Then, the analytic expressions of the distributions of lifetime and product reliability are presented. Significant findings include that the proposed TEDP model substantially enhances predictive accuracy in lifetime estimations of mechanical systems under varying operational conditions, as demonstrated in a practical case study on fatigue crack data. This model not only provides highly accurate lifetime predictions, but also offers deep insights into the reliability assessments of mechanically stressed components.

Identification of sensitive endpoints for the assessment of phthalates-induced reproductive and developmental toxicity: A literature mining study

Dibutyl phthalate (DBP) and di(2-ethylhexyl) phthalate (DEHP), two common types of phthalates, are known to cause reproductive and developmental toxicity in animals and humans. The reference doses (RfD) of DBP and DEHP should be determined by sensitive endpoints. We here aimed to identify sensitive endpoints for DBP- and DEHP-induced such toxicity using published literatures. By examining the impacts of maternal exposure to DBP or DEHP on anogenital distance (AGD) and semen quality of offspring, we discovered that DBP or DEHP caused AGD decline in boys but increase in girls with DBP being more potent and the first 14weeks of pregnancy being more susceptible, suggesting a chemical- and time-dependent phenomenon. We also identified AGD shortening and total sperm count reduction as two sensitive endpoints for DBP- or DEHP-induced reproductive and developmental toxicity, respectively. Based upon these two endpoints and the employment of the Bayesian benchmark dose approach with an uncertainty factor of 3,000, we estimated the RfD values of DBP and DEHP were 15 μg/kg/day and 36 μg/kg/day, respectively. Thus, we uncover previously unrecognized phenomena of DBP- or DEHP-induced reproductive and developmental toxicity and establish new and comparable or more conservative RfDs for the risk assessment of phthalates exposure in humans.

E-waste circular economy decision-making: a comprehensive approach for sustainable operation management in the UK

E-waste generation has broadly increased worldwide and is called intense pressure on sustainable practice implementation firms by recycling and redesigning the products. Thus, e-waste operation management in developed countries like the UK has become the top priority and is subjected to multiple sustainable circular economies (CE) contributing factors, including social, technical, environmental, and governmental policies. The authorized decision-makers can benefit from a well-established systematic decision-making tool to assess and evaluate the e-waste operation management considering the potential CE contributing factors. An extensive literature overview is expanded to identify the most relevant and influential contributing factors to e-waste CE. The city of London Metropolitan has been selected as the case location. In this regard, it is necessary to utilize an advanced multi-criteria decision-making tool to explore the interdependency and causality of CE-relevant factors. The present study proposed an innovative decision-making approach to address the multiple contributing factors of causality, interdependency, data, and model uncertainty in practice. It uses the step-wise weighted influence nonlinear gauge system method integrated with Fermatean fuzzy linguistic sets. This study conducted a sensitivity analysis to evaluate the effectiveness of the proposed decision-making approach in e-waste operation management. The results are promising, clearly demonstrating the framework’s competence. The CE index, crucial in designing e-waste operation management strategies, was calculated to be 2.8036. Among the various factors analyzed, “Environmental Management Systems” emerged as the most significant driving factor. This underscores the critical need to improve environmental management systems within e-waste operations.

FACTORS RELATED TO ANXIETY LEVEL IN ELDERLY RETIREMENTS IN SEROJA PUSKESMAS, BEKASI CITY 2022

Background: Aging is a process of slowly disappearing – gradually the ability of tissues to repair themselves or maintain their normal function so that they cannot survive the infection and repair the damage suffered. Anxiety the appearance of a fear or worry in something unclear and related to feelings of uncertainty and helplessness Factors causinganxiety in the elderly . Internal factors (biological, psychological, socio-cultural) : external factors (gender, age, social support, economic status). Researcher's Objective:Knowing the factors related to the level of anxiety in the retired elderly at the Seroja Kota Bekasi Health Center 2022. Research Method: The design in this study used Observational Analytics with a Cross Sectional research design, using independent variables of factors causing elderly packaging and a type of random sampling with a total sample of 88 respondents. The instruments in this research used digital questionnaires and interviews and were conducted using Chi Square. Researcher results:Based on statistical analysis with a significance level of 95% or a α value (0.05) chi square test results obtained p value (0.562) > α value (0.05) the results did not have a significant relationship between sex and anxiety level in respondents this showed Ha was rejected, 95% significance level or α value (0.05) Chi Square test results obtained p value (0.134) > α value (0.05) so that it can be concluded from these results there is no significant relationship between age and anxiety level in respondents this shows Ha is rejected, a significance level of 95% or a α value (0.05) chi square test results obtained p value (0.000) < α value (0.05) so that it can be concluded from these results there is a relationship between economic status and anxiety level in respondents this shows H0 is rejected, with a significance level of 95% or a value of α (0.05) chi square test results obtained p value (0.000) < value α (0.05) so that it can be concluded from these results there is a relationship between family support and the level of anxiety in respondents this shows that H0 is rejected. Thus it can be interpreted that "factors related to the level of anxiety in elderly retirees at the Seroja K Health Center ota Bekasi 2022". Conclusion: there is a relationship and there is no relationship between external factors, the level of anxiety level in the retired elderly at the Bekasi Health Center 2022.